by Anika D | Jan 17, 2022 | Emerging Technology, System Engineering
Overstaffing is a burden for a company, but shortages can be risky as it overwhelms the existing staff. So, staffing is a daunting task that needs to be balanced properly. Staff augmentation is one of the best ways to fill gaps in the workforce, keeping the company’s culture quality in mind.
It is a cost-effective model for staffing as you don’t have to spend money on office space or other overhead costs. In addition, it is an innovative approach to fill temporary positions with talent worldwide.
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What is Staff Augmentation?
Staff augmentation is one of the strategies of HR outsourcing. It allows companies to hire staff based on skills needed for any project to augment the team temporarily. It is a remote staffing model allowing organizations to make a team of skilled employees to perform a specific task and fill gaps in compliance with business objectives.
Ways to Use Staff Augmentation in the Company
The staff augmentation model is a highly effective staffing way if you implement it correctly.
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The following are some ways to augment your staff for the short-term with external resources:
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Select Projects with Limited Time Requirement
The model works well for one-time projects with no ongoing continuation plan. Usually, software development projects are viable options to avail the staff augmentation benefits. If you want to develop a mobile application or website to meet the business needs, go for staff augmentation instead of direct hiring.
There is a logic that technology and staff augmentation seem like a natural fit. Hiring a programmer on a project basis is a perfect decision compared to hiring a full-time developer for a one-time project. Mobile app or a website maintenance is a lengthy process, and you can do it in-house. Therefore, it is best to hire project managers and software developers using staff augmentation for IT business.
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Determine Talent Gaps within the Organization
You can look for insights related to talent gaps within the company by in-depth analysis of business processes. Perform a talent audit to identify the current talent deficits to evaluate existing project requirements or lay down new project plans.
After identifying gaps, determine whether there is any need for a resource on a long-term basis or limited to the specific project. For the latter one, staff augmentation is one of the best ways to bridge the gap.
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Identify Need for Unique Skill-Set
Once you determine a project role and particular skill-set you want to hire for, the process of looking for the right talent starts. The organization that you make your partner will look for the right talent by properly screening.
There is an advantage of hiring employees on a short-term basis. They have more experience handling projects similar to yours and showcase their own skill-set. The quality of work done by developers who worked on different projects is more than one who worked on the same project.
Wrapping Up
If you try staff augmentation, ensure that you have all those procedures in-house. First, create a detailed project plan. Then, follow it to set the tasks for your staff and manage the progress.
by Anika D | Jan 12, 2022 | General
Face recognition is becoming the mainstream technology used in sectors starting from law enforcement to biometric security. In addition, many organizations started using it in light of the COVID-19 pandemic.
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Facial recognition technology has a great potential to reduce physical contact or personal interactions and enforce COVID safety rules. Moreover, various IT giants offer cloud-based services like Microsoft Azure’s Face, Amazon Web Services Rekognition, etc.
Amazon’s Rekognition is a service that goes beyond face matching by detecting activities and understanding the movement. The ability to identify people and understand everything happening in a scene expands the technology’s use and business value.
Future of Face Recognition Technology
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● Face Detection
Face detection technology performs the following essential tasks to obtain better performance of face-related applications:
- Determining facial regions in a picture against different backgrounds.
- Determining the face alignment, like size, position, and rotation.
With the development of various face-related applications, face detection technology has become important in recent years to detect faces under challenging situations.
● Face Alignment
Facial pose and expressions impact the accuracy of face recognition. Therefore, it is vital to align the shape and position of facial parts for accurate face recognition. So, it is necessary to have a robust
● Face Matching
Face matching technology extracts a vector feature from a face image to identify the pre-registration of the person in the photo. However, the conditions of registered and query images captured are not always the same.
Variations of illumination or posture and facial expressions constitute significant factors to match performance degradation. To solve this pose variation problem, you can use face normalization technology. It corrects the frontal face posture as well as the size and position of an image by using a 3D shape model.
Use Cases
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The two critical applications for facial recognition technology are authentication and surveillance. Though both have a range of use cases, authentication has got acceptance by the public. It is convenient to outweigh the privacy implications.
The ease of unlocking Windows computers and smartphones with apps logging and authenticating transactions has made people comfortable with the technology. Meanwhile, it is also used in agreement with smart passports, speeding the immigration queues.
During COVID-19, most businesses apply facial recognition to build access management in the COVID-safe plan to return to work. It eliminates guest’s and staff’s requirement to check-in through communal touchscreen kiosks.
Face recognition technology can be integrated with self-service checkouts, supporting payments authorization or loyalty cards for completely touchfree transactions in the retail environment. In addition, various fraud detection applications in the financial sector are using this technology for two-factor authentication.
Also, some supermarkets are planning to encourage customers to ‘Just Walk Out’ with shopping bags by paying automatically. It is all thanks to artificial intelligence advances and facial recognition. The face recognition technology enables supermarkets to make completely checkout-less stores and minimal staff.
Conclusion
The future of face recognition technology is bright. It will indeed stay for a long time, so we should embrace its benefits. As seen above, various use cases show that facial recognition helps a lot. For example, it creates a safe environment, providing great security and enhancing customer experiences.
by Anika D | Jan 10, 2022 | Industrial IoT
The IOT is significantly growing in the industry and has a great scope. It is a network of people or physical objects called “things” embedded with software, networks, electronics, and sensors. It allows these objects to exchange and collect data to extend the Internet connectivity from mobile, computer, tablet, to other devices. Several industries use the IoT (Internet of things) in smart cities, smart homes, IoT e-commerce chains, farming, and manufacturing.
The following are some of the fascinating companies involved in the Internet of things worldwide:
GE Digital
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GE Digital aims to be one of the world’s most well-known innovators. It focuses on IOT solutions for disrupting industries like energy, manufacturing, telecommunications, and power generation. In addition, it offers customers problems and domain expertise to the industry. It uses analytics and digital twin capabilities to extract beneficial business insights. In addition, it helps us to tie insights with business outcomes through automating workflows. So, the company has various notable projects or achievements under its belt.
IBM Corporation
IBM Corporation is one of the biggest computing giants based in Armonk, NY, developing its own IBM Watson (IoT Platform). It focuses on being secure, simple, and scalable. This IoT Platform service adds analytic and blockchain services for the proper organization of device data into practical applications. In addition, this IoT platform allows designers to benefit from IoT services on large and small scales.
Amazon Web Services
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AWS brings an incredible array of machine learning and data analysis tools with several frameworks for IOT insights. Of course, it is now one of the best cloud providers that offer themselves as a general-purpose IoT back-end. However, AWS is the first to pull these elements and set the standard reasonably. Though it is difficult to sense an overall market share for AWS products, the enterprise IoT users survey rated them third among IoT platforms.
Amazon’s IOT offerings focus heavily on back-end storage, general and analysis management of the data that came from IoT implementations. So, people will not buy sensors, networking elements or edge hardware from Amazon. But AWS will handle the data manipulation.
Cisco
Cisco didn’t have the in-house expertise for advancing its IoT portfolio, so Cisco acquired it. Also, the company is applying its networking expertise to IoT-specific issues and integrating connectivity options smoothly.
CISCO produces a networking control suite and an IOx version for edge devices and ruggedized wireless gear mainly for industrial environments. It is one of the most prominent players. It made its presence seen in major IoT market segments, including automotive, gas and oil, and healthcare. In addition, it has an active presence in many parts of the IoT stack.
Microsoft Corporation
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Microsoft Corporation is one of the famous tech giants that have thrown their hat in the IoT space. They use their Microsoft Azure cloud management services for creating an IoT platform to operate with billions of IoT assets. Currently, Microsoft is investing a significant amount in IoT research. Moreover, it is a leading company of this technology.
Conclusion
The companies mentioned above offer different solutions that help businesses explore revenue streams and solve digital challenges with IoT technology.
by Anika D | Jan 5, 2022 | General
Artificial Intelligence (AI) will be the touchstone that transforms many industries. This brings doubts regarding the role of women in AI. Its predecessor, Information Technology, suffered from the lesser participation of women in leadership positions. This is poised to change with women’s role as frontrunners in AI.
Let’s look at the top seven women leading the AI world.
Joy Buolamwini
Joy Buolamwini, an MIT graduate, is lauded for her groundbreaking research on algorithm bias. She single-handedly exposed the race and gender biases implicit in facial recognition algorithms.
In addition, this acknowledged the concession that facial recognition technology was not ready for public consumption yet. This did not stop the other commercial applications of artificial intelligence. Also, Buolamwini founded an organization called ‘Algorithmic Justice League.’
Rana el Kaliouby
Rana el Kaliouby has made it her life’s work to improve the emotional intelligence of AI. She has transformed the field of expression recognition research. Hence, the technology development is a subdivision of facial recognition, focused on emotion recognition. Kaliouby is ascribed to the pioneering field of Emotion AI.
Moreover, she is co-founder and CEO of Affectiva, an MIT spinout company that created machine learning algorithms that interpret human expressions and emotions. MIT recently published a study on a new machine learning model that understands object relationships.
Daphne Koller
Koller has been a professor at Stanford since 1995, specializing in Machine Learning. She and her colleague, Andrew Ng, were the founders of the edu-tech business behemoth ‘Coursera.’ Most recently, Koller is the Founder-CEO of Insitro. It’s a firm that uses machine learning to revolutionize pharmacological medication research.
Anna Patterson
Anna Patterson is the founder and Managing Partner of Gradient Ventures. Her remarkable career delved into designing and delivering artificial intelligence to startups and major tech companies. Also, she started her career with Google and was promoted to the company’s VP of Engineering for many years.
Daniela Rus
She is the first female head of MIT’s Computer Science and Artificial Intelligence Lab (CSAIL). This is one of the most prestigious and prominent labs in the world. Her breakthrough research shows its impact on networked collaborative robots, self-configurable robots, and soft robots. In addition, she believes that a robot is a tool and can be extremely beneficial. Moreover, her efforts have led to artificial intelligence used everywhere.
Claire Delauney
Claire Delaunay has held leadership roles in many of Silicon Valley’s renowned firms. Her resume spans work with SRI, Google, Uber, and NVIDIA. Also, she is the co-founder of Otto. In addition, she is currently working on a platform that allows autonomous machines to be deployed at scale.
Fei Fei Li
She founded and managed the ImageNet initiative. It’s a repository of millions of tagged photos that have altered the course of AI. in addition, it formed the basis of deep learning. Moreover, reports say that Geoff Hinton and his colleagues used their neural network-based model AI trained on ImageNet.
Finally, artificial intelligence is changing the way you work. Moreover, the potential for what is possible. Also, it’s an avenue for women-led growth and development. These extraordinary women are trailblazing the way to the future.
by Anika D | Jan 3, 2022 | Artificial Intelligence, Machine Learning
The capabilities of machine learning increasingly influence the world. Machine learning is expanding the boundaries of what was once thought to be possible. It has permeated our daily lives through the apps we use to automate our daily schedule.
Machine Learning is a big part of pulling information from datasets. Thanks to the volume of data the algorithm is exposed to, they’re used in predicting patterns. This algorithm identifies emerging trends and translates data into consumers’ behavior information.
Let’s look at five real-world examples of machine learning in 2021.
Product Recommendations
This is the easily identifiable element of machine learning. If you’ve used Netflix, then you’re aware of the ‘Since you’ve watched ***’ feature. The Netflix algorithm makes recommendations about the shows you might like. This is also the case with Amazon. It shows you ‘customers who bought this item also bought.
This is possible due to the efforts of machine learning. You can also learn about machine learning through various courses.
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Speech Recognition
Voice-activated virtual assistants like Alexa, Siri, and Google Assistant have fine-tuned speech recognition. Machine learning can also facilitate speech-to-text functions. This has real-world applications in voice search, increasing the accessibility options for disabled people.
Another facet of machine learning is Neural Machine Learning, which can seamlessly translate from one language to another. Moreover, the ability of a computer program to understand spoken and written language is known as Natural Language Processing. It facilitates language-related tasks.
Medical Diagnostication
Thanks to machine learning, some chatbots can identify symptoms. You have algorithms to build models predicting 3D molecule drugs that discover life-saving medicine. Also, identifying patterns can help formulate diagnoses or create treatment plans for patients. It has a strong link with oncology and predicting if cells are cancerous or calculating the potential to be cancerous.
Self Driving Cars
Many can now experience the benefits of autonomous driving thanks to machine learning. Combining machine learning, sensors, and dynamic software has brought this experience to life and the market. This is thanks to the predictive analytical capability of machine learning.
In addition, as a promising field to lean into, it has the potential to change every industry from identifying fraudulent transactions to creating self-learning robotic process automation.
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Automated Arbitrage
Machine learning finds a new niche in finance. Arbitrage is buying or selling assets to generate profits from the price difference. The automation element comes into play when machine learning is used to analyze the data generated by trade. This helps in creating a trading algorithm.
Moreover, this trading algorithm can identify the patterns created in the market to identify profitable trades. You can use this algorithm to make real-time trading decisions leveraging these advantageous arbitrage opportunities.
Machine learning is changing the face of industries in many subtle but impactful ways. We’re only beginning to scratch the surface of what is possible with the use of machine learning. It has the potential to transform everything from healthcare to the economy. In addition, machine learning is going to have a hand in shaping the possible technology of the future.
